Title :
Short term load forecasting based on the particle swarm optimization with simulated annealing
Author_Institution :
Sch. of Innformation Sci. & Eng., Shandong Agric. Univ., Taian, China
Abstract :
This paper presented an artificial neural network (ANN) method based on the particle swarm optimization (PSO) and simulated annealing (SA) for load forecasting. Using the modified PSO with SA train the ANN network and facilitate the tuning of the optimal network weight and threshold. The ANN network has a better ability to escape from the local optimum and is more effective than the conventional PSO-based ANN. Then use the network to forecast the daily load. Simulation example shows that the proposed approach has good accuracy.
Keywords :
learning (artificial intelligence); load forecasting; neural nets; particle swarm optimisation; power engineering computing; simulated annealing; ANN training; artificial neural network; particle swarm optimization; short term load forecasting; simulated annealing; Artificial neural networks; Forecasting; Load forecasting; Load modeling; Particle swarm optimization; Predictive models; Artificial Neural Network; Particle Swarm Optimization; Short Term Load Forecasting; Simulated Annealing;
Conference_Titel :
Control Conference (CCC), 2011 30th Chinese
Conference_Location :
Yantai
Print_ISBN :
978-1-4577-0677-6
Electronic_ISBN :
1934-1768